Dissolution Curve Comparisons Through theF2Parameter, a Bayesian Extension of thef2Statistic

Dissolution Profiles Comparison Using Conventional and Bias Corrected and Accelerated f2 Bootstrap Approaches with Different Software's: Impact of Variability, Sample Size and Number of Bootstraps

Dissolution profiles comparison is an important element in order to support biowaivers, scale-up and post approval changes and site transfers. Highly variable dissolution can possess significant challenges for comparison and f2 bootstrap approach can be utilized in such cases. However, availability of different types of f2 and confidence intervals (CI) methods indicates necessity to understand each type of calculation thoroughly. Among all approaches, bias corrected and accelerated (BCa) can be an attractive choice as it corrects the bias and skewness of the distribution. In this manuscript, we have performed comparison of highly variable dissolution data using various software's by adopting percentile and BCa CI approaches. Diverse data with different variability's, number of samples and bootstraps were evaluated with JMP, DDSolver, R-software, SAS and PhEq. While all software's yielded similar observed f2 values, differences in lower percentile CI was observed. BCa with R-software and JMP provided superior lower percentile as compared to other computations. Expected f2 recommended by EMA has resulted as stringent criteria as compared to estimated f2. No impact of number of bootstraps on similarity analysis was observed whereas number of samples increased chance of acceptance. Variability has impacted similarity outcome with estimated f2 but chance of acceptance enhanced with BCa approach. Further, freely available R-software can be of attractive choice due to computation of various types of f2, percentile and BCa intervals. Overall, this work can enable regulatory submissions to enhance probability of similarity through appropriate selection of number of samples, technique based on variability of dissolution data.

Holistically assessing the quality consistency of compound liquorice tablets from similarities of both all chemical fingerprints and the integrated dissolution curves by systematically quantified fingerprint method

In recent years, traditional analytical methods have failed to meet the widespread use of multi-component Chinese pharmaceutical formulations. To solve this problem, this study proposed a comprehensive analytical strategy using compound liquorice tablets (CLTs) as an example, both in terms of chemical quality and dissolution curve consistency. Firstly, the peak purity of the two wavelengths was checked using dual-wavelength absorbance coefficient ratio spectra (DARS) to avoid the fingerprint bias caused by peak purity. Secondly, liquid-phase dual-wavelength tandem fingerprint (DWTF) of 38 batches of CLTs was established for the first time. The two analytical methods were also evaluated using the systematically quantified fingerprint method (SQFM), and the 38 batches of samples were classified into two grades with good quality consistency. Quantitative analysis of the five markers of CLTs was performed simultaneously using the standard curve method (SCM) and the quantitative analysis of multiple components by single marker (QAMS). The results showed no significant differences between the two analytical methods (p > 0.5). In addition, the in vitro dissolution of CLTs in two media (pure water and pH = 4.5 medium) was determined by the total UV fingerprint dissolution assay. The similarity of the dissolution curves was also analyzed by combining the f₂ factor and the dissolution-systematically quantified fingerprint method (DSQFM). The result showed that most of the samples had f₂ > 50 and P_m satisfied the range of 70-130%. Finally, a principal component analysis (PCA) model was developed to combine the evaluation parameters of chemical fingerprint and dissolution curves for comprehensive analysis of the samples. In this study, a chromatographic and dissolution-based quality analysis method was proposed, which effectively overcomes the shortcomings of previous analytical methods and provides a scientific analytical method for the quality control of natural drugs.

Statistical modeling approaches for the comparison of dissolution profiles

Dissolution studies are a fundamental component of pharmaceutical drug development, yet many studies rely upon the f 1 and f 2 model-independent approach that is not capable of accounting for uncertainty in parameter estimation when comparing dissolution profiles. In this paper, we deal with the issue of uncertainty quantification by proposing several model-dependent approaches for assessing the similarity of two dissolution profiles. We take a statistical modeling approach and allow the dissolution data to be modeled using either a Dirichlet distribution, gamma process model, or Wiener process model. These parametric forms are shown to be reasonable assumptions that are capable of modeling dissolution data well. Furthermore, based on a given statistical model, we are able to use the f 1 difference factor and f 2 similarity factor to test the equivalency of two dissolution profiles via bootstrap confidence intervals. Illustrations highlighting the success of our methods are provided for both Monte Carlo simulation studies, and real dissolution data sets.

DISSOLUTION PROFILE SIMILARITY ANALYSES-STATISTICAL PRINCIPLES, METHODS AND CONSIDERATIONS

The pharmaceutical industry and regulatory agencies rely on dissolution similarity testing to make critical product decisions as part of drug product life cycle management. Accordingly, the application of mathematical approaches to evaluate dissolution profile similarity is described in regulatory guidance with the emphasis given to the similarity factor f₂ with little discussion of alternative methods. In an effort to highlight current practices to assess dissolution profile similarity and to strive toward global harmonization, a workshop entitled "In Vitro Dissolution Similarity Assessment in Support of Drug Product Quality: What, How, When" was held on May 21-22, 2019 at the University of Maryland, Baltimore. This manuscript provides in-depth discussion of the mathematical principles of the model-independent statistical methods for dissolution profile similarity analyses presented in the workshop. Deeper understanding of the testing objective and statistical properties of the available statistical methods is essential to identify methods which are appropriate for application in practice. A decision tree is provided to aid in the selection of an appropriate statistical method based on the underlying characteristics of the drug product. Finally, the design of dissolution profile studies is addressed regarding analytical and statistical recommendations to sufficiently power the study. This includes a detailed discussion on evaluation of dissolution profile data for which several batches per reference and/or test product are available.

The current state of Bayesian methods in nonclinical pharmaceutical statistics: Survey results and recommendations from the DIA/ASA-BIOP Nonclinical Bayesian Working Group

The use of Bayesian methods to support pharmaceutical product development has grown in recent years. In clinical statistics, the drive to provide faster access for patients to medical treatments has led to a heightened focus by industry and regulatory authorities on innovative clinical trial designs, including those that apply Bayesian methods. In nonclinical statistics, Bayesian applications have also made advances. However, they have been embraced far more slowly in the nonclinical area than in the clinical counterpart. In this article, we explore some of the reasons for this slower rate of adoption. We also present the results of a survey conducted for the purpose of understanding the current state of Bayesian application in nonclinical areas and for identifying areas of priority for the DIA/ASA-BIOP Nonclinical Bayesian Working Group. The survey explored current usage, hurdles, perceptions, and training needs for Bayesian methods among nonclinical statisticians. Based on the survey results, a set of recommendations is provided to help guide the future advancement of Bayesian applications in nonclinical pharmaceutical statistics.

In Vitro Dissolution Profiles Similarity Assessment in Support of Drug Product Quality: What, How, When-Workshop Summary Report

The pharmaceutical industry and regulatory agencies rely on dissolution similarity testing to make critical product performance decisions as part of drug product life cycle management. Accordingly, the application of mathematical approaches to evaluate dissolution profile similarity is described in regulatory guidance. However, the requirements (e.g., which time points, number of time points, %CV) to apply the widely known similarity factor f₂ and other alternative statistical approaches diverge noticeably across regulatory agencies. In an effort to highlight current practices to assess dissolution profile similarity and to strive towards global harmonization, a workshop entitled "in vitro dissolution similarity assessment in support of drug product quality: what, how, when" was held May 21-22, 2019, at the University of Maryland, Baltimore. This article summarizes key points from the podium presentations and breakout (BO) sessions focusing on (1) contrasting the advantages and disadvantages of several statistical methods; (2) the importance of experimental design for successful similarity evaluation; (3) the value of similarity evaluation in light of clinically relevant specifications; and (4) the need for creating a robust and scientifically appropriate path (e.g., non-prescriptive decision tree) for dissolution profile similarity assessment as a stepping stone for global harmonization.